Staged diffusion in urban mobility and its quantum-walk characterization

This record contains the data and Python code supporting the manuscript Staged diffusion in urban mobility and its quantum-walk characterization. The study examines mobility diffusion across 12 cities and provides the processed numerical results needed to inspect the reported diffusion scaling, diffusion-regime transition, behavioral correlates, continuous-time quantum-walk (CTQW) fits, statistical analyses, and parameter-sensitivity tests. Deposited data City- and cluster-level empirical mean squared displacement (MSD) curves and instantaneous diffusion-exponent series used to identify super-diffusive and sub-diffusive regimes and estimate transition times. Diffusion-regime transition summaries and city-cluster behavioral samples for gap time, step length, and step speed. These files support the uncertainty estimates, before/after-transition comparisons, and Wilcoxon signed-rank tests reported in Fig. 4, Figs. S7-S19, and the associated supplementary tables. The larger behavioral files are divided into one archive per city for reliable downloading. Road-network shapefiles, final single-origin CTQW-generated MSD curves, fitted parameters, complete-curve R-squared values, temporal holdout results, and summary files underlying Fig. 6 and Tables S9-S11. Contextual-variable inputs for the transition-time Ridge analysis and compact source-data files underlying the revised main and supplementary figures and Tables S1-S12. Results of sensitivity analyses covering coordinate perturbation, rectangular trajectory-simplification parameters R and W, numerical differentiation settings, and alternative MSD temporal grids, including the source data for Fig. S22 and Table S12. Deposited code and documentation The code archive follows the analysis workflow: trajectory preprocessing and rectangular simplification; radius-of-gyration clustering; empirical MSD and diffusion- exponent estimation; diffusion-regime transition and behavioral-metric extraction; road-network preparation and CTQW fitting; statistical analysis; figure generation; and sensitivity analysis. The README explains the software environment and execution requirements. FIGURE_CODE_DATA_MAP.md links each main and supplementary figure to its generating script and deposited input data, while WORKFLOW_TO_FILES.md, MANIFEST.tsv, and SHA256SUMS.txt provide workflow, file-level, and integrity documentation. Data-access scope Coordinate-level trajectories are not redistributed in this record because the underlying city datasets originate from multiple third-party providers and require separate verification of redistribution and privacy conditions. Public source datasets and their access routes are cited in Table S1 of the manuscript. The deposited derived and source data are sufficient to inspect the numerical results and reproduce the main statistical summaries and figures without distributing individual coordinate records.